-
Notifications
You must be signed in to change notification settings - Fork 0
/
Code.py
80 lines (58 loc) · 2.18 KB
/
Code.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
# Import necessary libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
# Read training data from a CSV file
train_data = pd.read_csv("/content/drive/MyDrive/Linear_regression/Linear_regression_train.csv")
# Extract the 'x' feature for training
x_train = train_data[['x']]
# Extract the target variable 'y' for training
y_train = train_data['y']
# Read test data from a CSV file
test_data = pd.read_csv("/content/drive/MyDrive/Linear_regression/Linear_regression_test.csv")
# Extract the 'x' feature for testing
x_test = test_data['x']
# Extract the target variable 'y' for testing
y_test = test_data['y']
# Create a Linear Regression model
model = LinearRegression()
# Fit the model using the training data
model.fit(x_train, y_train)
# Predict 'y' for the training data
y_train_pred = model.predict(x_train)
# Reshape the test data for prediction
x_test = x_test.values.reshape(-1, 1)
# Predict 'y' for the test data
y_test_pred = model.predict(x_test)
print(y_test_pred)
# Calculate Mean Squared Error for the training set
mse_train = mean_squared_error(y_train, y_train_pred)
# Calculate R^2 Score for the training set
r2_train = r2_score(y_train, y_train_pred)
# Calculate Mean Squared Error for the test set
mse_test = mean_squared_error(y_test, y_test_pred)
# Calculate R^2 Score for the test set
r2_test = r2_score(y_test, y_test_pred)
# Print results for the training set
print("Training Set:")
print(f"Mean Squared Error: {mse_train:.2f}")
print(f"R^2 Score: {r2_train:.2f}")
# Print results for the test set
print("\nTest Set:")
print(f"Mean Squared Error: {mse_test:.2f}")
print(f"R^2 Score: {r2_test:.2f}")
# Plot the actual test data points
plt.scatter(x_test, y_test, color='black', label="Actual data")
# Plot the regression line using predicted values
plt.plot(x_test, y_test_pred, color='red', linewidth=3, label="Regression Line")
# Set labels and title for the plot
plt.xlabel('X')
plt.ylabel('y')
plt.title('Linear Regression - Test Set')
# Display the legend
plt.legend()
# Show the plot
plt.show()